bigquery-magics 0.10.1

Google BigQuery magics for Jupyter and IPython

Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery solves this problem by enabling super-fast, SQL queries against append-mostly tables, using the processing power of Google’s infrastructure.

• Library Documentation

• Product Documentation

Quick Start

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.

2. Enable billing for your project.

3. Enable the Google Cloud BigQuery API.

4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.7

Unsupported Python Versions

Python == 3.5, Python == 3.6.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install bigquery-magics

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install bigquery-magics

Example Usage

To use these magics, you must first register them. Run the %load_ext bigquery_magics in a Jupyter notebook cell.

%load_ext bigquery_magics

Perform a query

%%bigquery
SELECT name, SUM(number) as count
FROM 'bigquery-public-data.usa_names.usa_1910_current'
GROUP BY name
ORDER BY count DESC
LIMIT 3

Since BigQuery supports Python via BigQuery DataFrames, %%bqsql is offered as an alias to clarify the language of these cells.

%%bqsql
SELECT name, SUM(number) as count
FROM 'bigquery-public-data.usa_names.usa_1910_current'
GROUP BY name
ORDER BY count DESC
LIMIT 3